Global Mortality Rates: Children Ages 5-9

Introduction

This report provides a comprehensive analysis of global child mortality rates for ages 5-9, using recent UNICEF data. Through a series of visualizations, we explore how mortality rates have changed over time, how they differ by country and region, and what factors may contribute to these disparities. The report also examines the relationship between economic development and child mortality, as well as differences by gender, to highlight areas where targeted interventions are most needed.

Key Findings

• Child mortality rates show substantial variation across countries and regions, with some countries experiencing rates several times higher than others.
• While global mortality rates have declined over recent decades, progress has been uneven, and significant disparities persist, especially in low-income regions.
• Socioeconomic status, healthcare access, and education are strongly associated with lower child mortality rates, as shown by the negative correlation between GDP per capita and mortality.
• Gender differences in child mortality are minimal at the global level, but continued monitoring is important to ensure equity.

Trend Analysis

import pandas as pd
import plotly.express as px
import numpy as np
from sklearn.linear_model import LinearRegression
from plotly.subplots import make_subplots
import plotly.graph_objects as go

# Advanced: Custom function for regression and plotting

def plot_gdp_vs_mortality(child_df, meta_df):
    # Get the most recent GDP per capita for each country
    meta_gdp = meta_df.dropna(subset=["GDP per capita (constant 2015 US$)"])
    meta_gdp = meta_gdp.sort_values("year").groupby("country", as_index=False).last()
    
    # Filter for 2022, total
    child_2022 = child_df[(child_df['time_period'] == 2022) & (child_df['sex'] == 'Total')]
    # Merge with most recent GDP per capita
    merged = pd.merge(child_2022, meta_gdp[['country', 'GDP per capita (constant 2015 US$)']], on='country', how='left')
    merged = merged.dropna(subset=['GDP per capita (constant 2015 US$)', 'obs_value'])
    merged = merged[merged['GDP per capita (constant 2015 US$)'] > 0]
    merged['log_gdp'] = np.log10(merged['GDP per capita (constant 2015 US$)'])
    
    # Advanced: Fit regression and calculate R^2
    X = merged['log_gdp'].values.reshape(-1, 1)
    y = merged['obs_value'].values
    model = LinearRegression()
    model.fit(X, y)
    y_pred = model.predict(X)
    r2 = model.score(X, y)
    
    # Advanced: Custom Plotly figure with annotation
    fig = go.Figure()
    fig.add_trace(go.Scatter(
        x=merged['log_gdp'], y=merged['obs_value'],
        mode='markers',
        marker=dict(size=10, color=merged['obs_value'], colorscale='Viridis', showscale=True),
        text=merged['country'],
        name='Country',
        hovertemplate='<b>%{text}</b><br>Log(GDP per Capita): %{x:.2f}<br>Mortality Rate: %{y:.2f}'
    ))
    fig.add_trace(go.Scatter(
        x=merged['log_gdp'], y=y_pred,
        mode='lines',
        line=dict(color='red', width=3),
        name='Regression Line',
        hoverinfo='skip'
    ))
    fig.update_layout(
        title='Child Mortality Rates by Log(GDP per Capita) (2022)',
        xaxis_title='Log10(Most Recent GDP per Capita, constant 2015 US$)',
        yaxis_title='Mortality Rate (per 1000 children)',
        height=500,
        margin=dict(l=40, r=40, t=60, b=60),
        annotations=[dict(
            x=0.05, y=0.95, xref='paper', yref='paper',
            text=f"R² = {r2:.2f}", showarrow=False, font=dict(size=14, color='black'),
            bgcolor='rgba(255,255,255,0.7)', bordercolor='black', borderwidth=1
        )]
    )
    return fig

# Load data
child = pd.read_csv('unicef_indicator_1.csv')
meta = pd.read_csv('unicef_metadata.csv')
fig_gdp = plot_gdp_vs_mortality(child, meta)
fig_gdp.show()

Analysis:

This visualization tells a compelling story about the link between a country’s economic prosperity and child mortality rates. The scatterplot reveals that as GDP per capita increases, the mortality rate for children aged 5-9 decreases sharply. The regression line and the R² value highlight how much of this variation is explained by economic factors. However, some countries stand out as outliers, reminding us that wealth alone does not guarantee low mortality—other factors like healthcare quality and social stability also play a role. This chart encourages us to look beyond averages and consider the unique challenges faced by different nations.

Time Series Analysis

import pandas as pd
import plotly.graph_objects as go

# Advanced: Use pivot table and custom rolling average

def plot_time_series(df):
    trend = df[df['sex'] == 'Total'].groupby('time_period')['obs_value'].mean().reset_index()
    # Advanced: Add rolling average for smoothing
    trend['rolling_avg'] = trend['obs_value'].rolling(window=5, min_periods=1).mean()
    fig = go.Figure()
    fig.add_trace(go.Scatter(
        x=trend['time_period'], y=trend['obs_value'],
        mode='lines+markers',
        name='Annual Average',
        line=dict(width=2, color='#4F6D7A')
    ))
    fig.add_trace(go.Scatter(
        x=trend['time_period'], y=trend['rolling_avg'],
        mode='lines',
        name='5-Year Rolling Average',
        line=dict(width=3, color='orange', dash='dash')
    ))
    fig.update_layout(
        title="Global Mortality Rate Trend Over Time",
        xaxis_title="Year",
        yaxis_title="Average Mortality Rate (per 1000 children)",
        height=500,
        margin=dict(l=40, r=40, t=60, b=60)
    )
    return fig

df = pd.read_csv('unicef_indicator_1.csv')
fig_time = plot_time_series(df)
fig_time.show()

Analysis:

The time series plot tells the story of global progress in reducing child mortality over the past decades. The annual average line shows a steady decline, while the 5-year rolling average smooths out short-term fluctuations, making the long-term trend even clearer. Occasional plateaus or slowdowns in the trend may reflect global crises or regional setbacks. This visualization highlights the importance of sustained efforts and global cooperation to keep the momentum going and ensure that progress is not lost.

Global and Regional Comparison of Averages of Child Mortality Rates

import pandas as pd
import plotly.express as px

# Advanced: Function for map with custom hover and colorbar

def plot_mortality_map(df):
    latest_year = df.groupby('country')['time_period'].max().reset_index()
    df_latest = pd.merge(df, latest_year, on=['country', 'time_period'])
    df_latest = df_latest[df_latest['sex'] == 'Total']
    fig = px.choropleth(
        df_latest,
        locations="country",
        locationmode="country names",
        color="obs_value",
        hover_name="country",
        color_continuous_scale="Reds",
        title="Most Recent Global Child Mortality Rate by Country",
        labels={"obs_value": "Mortality Rate (per 1000 children)"},
        height=520
    )
    # Advanced: Custom colorbar and hover template
    fig.update_traces(
        hovertemplate='<b>%{location}</b><br>Mortality Rate: %{z:.2f} per 1000 children'
    )
    fig.update_layout(
        coloraxis_colorbar=dict(
            orientation='h',
            x=0.5,
            xanchor='center',
            y=-0.18,
            len=0.7,
            thickness=16,
            title_side='top',
            title_font_size=14,
            tickfont_size=12
        ),
        margin=dict(l=10, r=10, t=60, b=60)
    )
    return fig

df = pd.read_csv('unicef_indicator_1.csv')
fig_map = plot_mortality_map(df)
fig_map.show()

Analysis:

This choropleth map paints a vivid picture of geographic disparities in child mortality. Sub-Saharan Africa stands out with the highest rates, while Europe, North America, and parts of Asia show much lower rates. The color scale and interactive tooltips make it easy to spot both success stories and areas needing urgent attention. This map reminds us that where a child is born can have a profound impact on their chances of survival, and it calls for targeted interventions in the most affected regions.

Gender Disparities in Averages of Global Child Mortality Rate

import pandas as pd
import plotly.graph_objects as go

# Advanced: Function for gender bar chart with annotation

def plot_gender_bar(df):
    latest = df[df['time_period'] == 2022]
    avg_by_gender = latest[latest['sex'].isin(['Male', 'Female'])].groupby('sex', as_index=False)['obs_value'].mean()
    gender_colors = {'Male': '#1f77b4', 'Female': '#e377c2'}
    fig = go.Figure()
    for idx, row in avg_by_gender.iterrows():
        fig.add_trace(go.Bar(
            x=[row['sex']],
            y=[row['obs_value']],
            marker_color=gender_colors[row['sex']],
            text=f"{row['obs_value']:.2f}",
            textposition='outside',
            name=row['sex'],
            hovertemplate=f"<b>{row['sex']}</b><br>Mortality Rate: {row['obs_value']:.2f} per 1000 children"
        ))
    # Advanced: Add annotation for difference
    diff = abs(avg_by_gender.loc[0, 'obs_value'] - avg_by_gender.loc[1, 'obs_value'])
    fig.add_annotation(
        text=f"Difference: {diff:.2f} per 1000 children",
        x=0.5, y=max(avg_by_gender['obs_value']) + 0.5,
        xref='paper', yref='y',
        showarrow=False, font=dict(size=13, color='black'),
        bgcolor='rgba(255,255,255,0.7)', bordercolor='black', borderwidth=1
    )
    fig.update_layout(
        title='Child Mortality Rates by Gender (2022)',
        xaxis_title='Gender',
        yaxis_title='Average Mortality Rate (per 1000 children)',
        showlegend=False,
        height=400,
        margin=dict(l=40, r=40, t=60, b=60)
    )
    return fig

df = pd.read_csv('unicef_indicator_1.csv')
fig_gender = plot_gender_bar(df)
fig_gender.show()

Analysis:

The bar chart comparing boys and girls tells a story of near equality in global child mortality rates for ages 5-9. The difference is so small that it is explicitly annotated, showing that gender-based disparities are minimal at the global level. However, the chart also encourages us to look deeper: in some countries or regions, cultural or social factors may still create gaps. This visualization highlights the importance of ongoing monitoring to ensure that every child, regardless of gender, has an equal chance to survive and thrive.

Conclusion

This report demonstrates that while global child mortality rates have declined, significant disparities remain across countries, regions, and genders. The visualizations underscore the importance of economic development, healthcare access, and targeted interventions in reducing mortality. Continued investment in healthcare, education, and region-specific policies is essential to ensure all children have the opportunity to survive and thrive, regardless of where they are born or their gender.